Disciplina Discipline VPS5732
Análise de Redes em Epidemiologia Veterinária
Network Analysis in Veterinary Epidemiology

Área de Concentração: 10134

Concentration area: 10134

Criação: 27/06/2024

Creation: 27/06/2024

Ativação: 27/06/2024

Activation: 27/06/2024

Nr. de Créditos: 4

Credits: 4

Carga Horária:

Workload:

Docentes Responsáveis:

Professors:

Marcos Amaku

José Henrique de Hildebrand e Grisi Filho

Objetivos:

Capacitar os alunos a analisar redes complexas, com vistas às aplicações em epidemiologia veterinária.

Objectives:

To enable students to analyze complex networks with a view to applications in veterinary epidemiology.

Justificativa:

Em anos recentes, a análise de redes complexas (e teoria dos grafos) passou a ser utilizada nas áreas biológicas, agrárias e de saúde com crescente intensidade. Esta ferramenta tem grande aplicação na análise de sistemas de relacionamentos entre entidades, seja uma rede de computadores conectados, um grupo de pessoas com interações sociais, difusão de informação entre animais, comércio de animais entre propriedades, comércio de animais entre países, etc. Especificamente, a ferramenta tem grande aplicação em epidemiologia, por permitir entender a estrutura de contatos entre indivíduos ou grupos de indivíduos, pelos quais uma doença é transmitida.

Rationale:

In recent years, the analysis of complex networks (and graph theory) has been used with increasing intensity in the biological, agricultural and health areas. This tool has wide application in the analysis of systems of relationships between entities, be it a network of connected computers, a group of people with social interactions, the dissemination of information between animals, trade in animals between farms, trade in animals between countries, etc. Specifically, the tool has wide application in epidemiology, as it allows us to understand the structure of contacts between individuals or groups of individuals, through which a disease is transmitted.

Conteúdo:

Redes complexas: conceitos e definições. Extração de rede a partir de um banco de dados de trânsito de animais. Cálculo (ou estimativa) de parâmetros de rede. Modelos de redes complexas (regulares, Poisson, pequeno mundo, livres de escala) e geração de redes aleatórias. Modelos epidêmicos em redes complexas: dinâmica e controle (SI, SIS, SIR, etc.). Identificação de comunidades. Lei de Pareto, Assortatividade e Paradoxo da Amizade. Aplicações de análise de redes em Epidemiologia.

Content:

Complex networks: concepts and definitions. Network extraction from an animal movement database. Calculating (or estimating) network parameters. Complex network models (lattice, Poisson, small-world, scale-free) and random network generation. Epidemic models in complex networks: dynamics and control (SI, SIS, SIR, etc.). Identification of communities. Pareto's Law, Assortativity and the Friendship Paradox. Applications of network analysis in Epidemiology.

Forma de Avaliação:

A nota final da disciplina será calculada a partir da fórmula: Nota final = avaliação do trabalho final A nota final será transformada em conceito final, conforme: - Conceito A: Excelente - de 8,0 a 10; - Conceito B: Bom - de 7,0 a 7,9; - Conceito C: Regular - de 6,0 a 6,9; - Conceito R: Reprovado por nota, menor de 6,0 e/ou por frequência, quando menor que 75%.

Type of Assessment:

The final grade for the course will be calculated using the formula: Final grade = evaluation of the final assignment The final grade will be transformed into a final concept, as follows: - Concept A: Excellent - from 8.0 to 10; - Concept B: Good - from 7.0 to 7.9; - Concept C: Fair - from 6.0 to 6.9; - Concept R: Fail by grade, less than 6.0 and/or by frequency, when less than 75%.

Observação:

1. A disciplina será 100% no sistema não presencial. 2. As aulas serão realizadas de forma síncrona, sendo gravadas, e o conteúdo disponibilizado possibilitando ao aluno assistir o conteúdo posteriormente. 3. Conteúdos complementares serão disponibilizados no ambiente virtual de aprendizagem. 4. A plataforma a ser utilizada será o Moodle (e-Disciplinas). As aulas virtuais serão realizadas no Google Meet. 5. A interação entre o aluno e os docentes se dará de modo online, durante as aulas, e por meio de email. 6. A frequência será determinada pela presença do aluno nas aulas virtuais. 7. É obrigatória a utilização de câmera e microfone. 8. A avaliação será realizada através de trabalho final e exercícios. 9. A universidade tem instalações equipadas com computadores que garantem aos alunos os meios de acessar o curso remotamente na indisponibilidade de meios próprios. 10. A disciplina utilizará os softwares R e RStudio.

Notes/Remarks:

1. Classes will be 100% online. 2. Classes will be held synchronously, being recorded and the recording made available, allowing the student to watch it later. 3. Complementary activities will be made available on the virtual learning environment. 4. The platform to be used will be Moodle (e-Disciplinas). Virtual classes will be held on Google Meet. 5. The interaction between the student and the teachers will take place online, during classes, and through email. 6. Attendance will be determined by the student's attendance at the virtual classes. 7. The use of camera and microphone is mandatory. 8. Assessment will be through a final assignment and exercises. 9. The university has facilities equipped with computers that guarantee students the means to access the course remotely, should their own means not be available. 10. The course will use R and RStudio software.

Bibliografia:

Christakis, N. A, & Fowler, J. H. (2010). Social network sensors for early detection of contagious outbreaks. PloS One, 5(9), e12948. Retrieved from http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2939797&tool=pmcentrez&rendertype=abstract Dubé, C., Ribble, C., Kelton, D., & McNab, B. (2009). A review of network analysis terminology and its application to foot-and-mouth disease modelling and policy development. Transboundary and Emerging Diseases, 56(3), 73–85. http://doi.org/10.1111/j.1865-1682.2008.01064.x Feld, S. L. (1991). Why Your Friends Have More Friends than You Do. American Journal of Sociology, 96(6), 1464–1477. Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3-5), 75–74. Retrieved from http://linkinghub.elsevier.com/retrieve/pii/S0370157309002841 Frössling, J., Ohlson, A., Björkman, C., Håkansson, N., & Nöremark, M. (2012). Application of network analysis parameters in risk-based surveillance - Examples based on cattle trade data and bovine infections in Sweden. Preventive Veterinary Medicine, 105(3), 202–8. http://doi.org/10.1016/j.prevetmed.2011.12.011 Langville, A. N., & Meyer, C. D. (2006). Google’s PageRank and Beyond: The Science of Search Engine Rankings. Princeton University Press. Martínez-López, B., Perez, A. M., & Sánchez-Vizcaíno, J. M. (2009). Social network analysis. Review of general concepts and use in preventive veterinary medicine. Transboundary and Emerging Diseases, 56(4), 109–20. http://doi.org/10.1111/j.1865-1682.2009.01073.x Newman, M. E. J. (2003). The Structure and Function of Complex Networks. SIAM Review, 45(2), 167–256. Retrieved from http://epubs.siam.org/doi/abs/10.1137/S003614450342480 Nöremark, M., & Widgren, S. (2014). EpiContactTrace: an R-package for contact tracing during livestock disease outbreaks and for risk-based surveillance. BMC Veterinary Research, 10, 71. http://doi.org/10.1186/1746-6148-10-71 Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank Citation Ranking: Bringing Order to the Web. Stanford InfoLab. Retrieved from http://infolab.stanford.edu/~backrub/pageranksub.ps

Bibliography:

Christakis, N. A, & Fowler, J. H. (2010). Social network sensors for early detection of contagious outbreaks. PloS One, 5(9), e12948. Retrieved from http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2939797&tool=pmcentrez&rendertype=abstract Dubé, C., Ribble, C., Kelton, D., & McNab, B. (2009). A review of network analysis terminology and its application to foot-and-mouth disease modelling and policy development. Transboundary and Emerging Diseases, 56(3), 73–85. http://doi.org/10.1111/j.1865-1682.2008.01064.x Feld, S. L. (1991). Why Your Friends Have More Friends than You Do. American Journal of Sociology, 96(6), 1464–1477. Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3-5), 75–74. Retrieved from http://linkinghub.elsevier.com/retrieve/pii/S0370157309002841 Frössling, J., Ohlson, A., Björkman, C., Håkansson, N., & Nöremark, M. (2012). Application of network analysis parameters in risk-based surveillance - Examples based on cattle trade data and bovine infections in Sweden. Preventive Veterinary Medicine, 105(3), 202–8. http://doi.org/10.1016/j.prevetmed.2011.12.011 Langville, A. N., & Meyer, C. D. (2006). Google’s PageRank and Beyond: The Science of Search Engine Rankings. Princeton University Press. Martínez-López, B., Perez, A. M., & Sánchez-Vizcaíno, J. M. (2009). Social network analysis. Review of general concepts and use in preventive veterinary medicine. Transboundary and Emerging Diseases, 56(4), 109–20. http://doi.org/10.1111/j.1865-1682.2009.01073.x Newman, M. E. J. (2003). The Structure and Function of Complex Networks. SIAM Review, 45(2), 167–256. Retrieved from http://epubs.siam.org/doi/abs/10.1137/S003614450342480 Nöremark, M., & Widgren, S. (2014). EpiContactTrace: an R-package for contact tracing during livestock disease outbreaks and for risk-based surveillance. BMC Veterinary Research, 10, 71. http://doi.org/10.1186/1746-6148-10-71 Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank Citation Ranking: Bringing Order to the Web. Stanford InfoLab. Retrieved from http://infolab.stanford.edu/~backrub/pageranksub.ps

Tipo de oferecimento da disciplina:

Não-Presencial

Class type:

Não-Presencial

Informações adicionais do oferecimento da disciplina:

A disciplina será 100% no sistema não presencial, com aulas realizadas de forma síncrona.

Additional class type information:

A disciplina será 100% no sistema não presencial, com aulas realizadas de forma síncrona.